Data Engineer

We are seeking an experienced Data Engineer to join our team. The Data Engineer will be responsible for extracting, transforming, and loading data from various sources into data lakes and data warehouses using AWS Glue while ensuring the efficiency and alignment of data systems with business goals. The ideal candidate should have strong analytical skills, familiarity with programming languages such as Python and R, SQL, and experience in building ETL/ELT pipelines.

Must Haves:

• Bachelors degree in Computer Science or equivalent required.
• 2+ years of progressive experience in working on AWS services.
• Previous experience as a data engineer or in a similar role.
• Technical expertise with data models, data scraping, data cleansing, and segmentation techniques.
• Knowledge and understanding of Amazon Web Services such as AWS Glue (Crawler, Job, Database, Workflow), AWS S3, AWS App flow, AWS Athena, AWS Lambda, etc.
• Knowledge and experience in connecting with multiple data sources using different AWS Services or APIs or connection protocols such as ODBC, JDBC, etc.
• Knowledge and experience of Python and PySpark.
• Knowledge and experience in SQL and SparkSQL queries.
• Knowledge of MS Excel and ability to build various views using pivot tables.
• Great numerical, statistical, and analytical skills.
• Data engineering certification will be a plus.
• Knowledge and experience in Beautiful Soup/Selenium/Scrappy will be a plus.
• Knowledge and experience on Terraform will be a plus.
  
  
  

Responsibilities:

• Extract data from multiple sources (Cloud/On-Prem) and ingest it into a data lake (AWS S3) through different AWS Services or APIs or connection protocols such as ODBC, JDBC, etc.
• Cleanse, transform, and maintain data quality in data lakes and data warehouses.
• Build and maintain data lakes, data warehouses, and data marts on AWS as per the business requirements.
• Build data catalogs on AWS Glue.
• Build data pipelines and workflows to ingest raw data and transform/clean data into data lakes and data warehouses respectively using AWS Glue.
• Conduct complex data analysis and report on results.
• Explore ways to enhance data quality and reliability.
• Evaluate business needs and objectives.
• Interpret trends and patterns.